Referral processes and wait times in primary care.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the response times to requests for consultations from FPs and the wait times for patient appointments. DESIGN: Mailed invitation to participate in a survey about non-FP specialist consultation requests from April 28 to May 9, 2014. SETTING: Hamilton, Ont. PARTICIPANTS: All active physicians with community practices from the Department of Family Medicine at St Joseph's Healthcare Hamilton and Hamilton Health Sciences. MAIN OUTCOME MEASURES: All non-FP specialist consultation requests for a 2-week period. RESULTS: Thirty-four practices (9.6% response rate) collected data on 816 consultation requests. Requests for referrals were most commonly made to the following 5 specialties: dermatology, surgery, gastroenterology, orthopedics, and obstetrics and gynecology. Overall, 36.4% of the requests for consultation received no response from the non-FP specialist's office by the end of the follow-up period. The mean wait time for a patient appointment was 60.1 days (range 23.3 to 168.5 days). Five specialties had particularly lengthy wait times of 105.9 to 168.5 days. CONCLUSION: Allowing 5 to 7 weeks for a response from a non-FP specialist, there was still a 36.4% nonresponse rate (similar to a pilot survey administered in 2010). Patient and physician frustration is certainly heightened and more office time and energy is expended when no acknowledgment of a referral is received within 7 weeks. This gives our community wait times much longer than those reported by any of the national bodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".